Stock Price Prediction Model for KLBF using Linear Regression Algorithm

Abstract

Stock prices are influenced by constantly changing supply and demand, leading to short-term price fluctuations. Other factors affecting stock prices include interest rates, inflation, company earnings, and marketing strategies. These price fluctuations increase the losses, making stock price predictions crucial to assist investors in making safer investment decisions. This research utilizes the Linear Regression algorithm to predict the stock prices of the pharmaceutical company with the stock code KLBF using a time series dataset in the period from January 2020 to January 2022. According to the experimental result, the proposed model can produce a total sum of squared errors is 27,105 with RMSE = 23.06. This low error margin indicates a strong predictive performance and the effectiveness of the proposed approach in predicting stock prices.